Marketing attribution is a mess.
How did it get so bad?
A five-part blog series, diving into marketing attribution across all of its iterations.
Under construction for the Channel99 blog.
Part One: How did it get so bad?
Somewhere along the way, attribution became less of a decision-making tool and more of a battleground. Instead of aligning teams, it drove them apart. Instead of improving marketing’s standing, it invited scrutiny. And instead of helping us make better decisions, it led to even more confusion about what’s actually moving the needle.
Marketing attribution is a mess.
Part Two: First touch
Marketing teams love a clear origin story.
First-touch attribution isn’t flawed. It’s just incomplete. It helps answer a very specific question: how are people finding us? But if you try to use it to explain why they bought, you’re asking too much. It’s still useful. Especially for campaign testing, early-stage programs, and channel discovery. But it should be a starting point, not a final answer.
The pros and cons of
first-touch attribution
Part Three: Last touch
Last-touch attribution may represent the “final straw,” but there were a lot of straws on that camel’s back.
The pros and cons of last touch attribution
Part Four: Multi-touch attribution (MTA)
More data was supposed to mean deeper insights, but bad or missing data broke that expectation.
In a world of fragmented buying committees, long sales cycles, and dozens of touchpoints per deal, MTA aims to reveal the cumulative influence of marketing. It’s not a silver bullet, but it remains a central part of many B2B marketers’ attribution strategies. Used thoughtfully, MTA can drive smarter decisions. Used carelessly, it can lead teams astray. Especially if there are problems with the data.
The pros and cons of multi-touch attribution
Part Five: AI-Based Attribution
Marketing attribution has always been like a jigsaw puzzle with missing pieces.
Traditional models like first-touch, last-touch, and multi-touch each offer partial answers, but none offer the satisfaction you get when you place that last piece.
Over time, marketers have pushed the limits of rule-based models in search of deeper insight. Now, artificial intelligence (AI) promises to move attribution beyond fixed models and toward dynamic, predictive frameworks that better reflect modern B2B buying.
But does AI really fix what’s broken, or does it simply add new layers of complexity?
